What to evaluate in Lyceum
Lyceum offers managed GPU compute and inference. Lyceum combines compute workloads and inference. Separate model API calls from GPU instances, containers, storage and dedicated endpoints before choosing what to migrate.
This is a comparison framework based on product documentation, not a claim of a hands-on performance benchmark.
Compare the whole model shortlist
Build a shortlist that includes your required proprietary and open-weight models. TextCortex lets you evaluate GPT, Claude and Gemini alongside Kimi, GLM, DeepSeek and MiMo through one client.
Map the exact identifiers and capabilities on each service. A similarly named model can have a different version, configuration or deployment; keep those differences visible in your results.
Read pricing as a workload calculation
Use current account rates rather than a single headline token price. Separate input and output usage, then include cache behavior, reasoning usage and retries where billed.
Keep dedicated capacity, subscription fees and optional services separate from per-request costs. Our API cost calculator works with the rates you enter.
Run a repeatable API evaluation
Use the same representative inputs and score the results before selecting a default.
- Record exact model identifiers and processing routes.
- Measure first-token time and completed-response time separately.
- Validate tool calls and output formats used by your application.
- Track error rates, token usage and successful results.
When TextCortex is the better fit
Choose TextCortex when a single integration across proprietary and open-weight models is central to the project, especially when you also need European model-hosting options.
Explore the Lyceum alternative page or follow the migration guide to make the comparison concrete.